Segment Anything Model (SAM)是一种在大型标注数据上训练的视觉基础模型，近年来在医学图像分割领域持续引起关注。尽管SAM在自然场景中表现出令人印象深刻的能力，但在面对医学图像时，尤其是涉及模糊边界和高度不规则的低对比度区域时，其性能会下降。本文提出了一种基于SAM的参数高效微调方法，称为SAMIHS，用于颅内出血分割，这是中风诊断和手术规划中关键且具有挑战性的步骤。与之前的SAM和基于SAM的方法不同，SAMIHS将参数重构适配器集成到SAM的图像编码器中，并考虑了适配器参数的高效灵活利用。此外，我们采用了一种组合损失函数，将二元交叉熵损失和边界敏感损失相结合，以增强SAMIHS识别边界区域的能力。我们在两个公共数据集上的实验结果证明了所提方法的有效性。代码可在https://github.com/mileswyn/SAMIHS获取。
英文摘要：
Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation. Despite the impressive capabilities of SAM on natural scenes, it struggles with performance decline when confronted with medical images, especially those involving blurry boundaries and highly irregular regions of low contrast. In this paper, a SAM-based parameter-efficient fine-tuning method, called SAMIHS, is proposed for intracranial hemorrhage segmentation, which is a crucial and challenging step in stroke diagnosis and surgical planning. Distinguished from previous SAM and SAM-based methods, SAMIHS incorporates parameter-refactoring adapters into SAM's image encoder and considers the efficient and flexible utilization of adapters' parameters. Additionally, we employ a combo loss that combines binary cross-entropy loss and boundary-sensitive loss to enhance SAMIHS's ability to recognize the boundary regions. Our experimental results on two public datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/mileswyn/SAMIHS .
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学北京天坛医院）
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended t…
University of California Berkeley（加州大学伯克利分校）；University of California San Francisco School of Medicine（加州大学旧金山分校医学院）
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans. We propose to solve both tasks as a semantic segmentation problem using a patch-based fully convolutional network (PatchFCN). This formulation allows us to accurately localize hemorrhages while bypassing the complexity of object detection. Our system demonstrates competitive performance with a human expert and the state-of-the-art on classification tasks (0.976, 0.966 AUC of ROC on retrospective and prospective test sets) and on segmentation tasks (0.785 pixel AP, 0.…
Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs. In this work, we describe our solution to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI, operating slice-wise without resampling. The final submission is an ensemble of 18 models. Our solution (team name NVAUTO) achieves the top place in terms of Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg.
We propose a feature generator backbone composed of an ensemble of convolutional neuralnetworks (CNNs) to improve the recently emerging Vision Transformer (ViT) models. We tackled the RSNA intracranial hemorrhage classification problem, i.e., identifying various hemorrhage types from computed tomography (CT) slices. We show that by gradually stacking several feature maps extracted using multiple Xception CNNs, we can develop a feature-rich input for the ViT model. Our approach allowed the ViT model to pay attention to relevant features at multiple levels. Moreover, pretraining the n CNNs using…
Capital Medical University（首都医科大学）；China National Clinical Research Center for Neurological Diseases（国家神经系统疾病临床医学研究中心）；Tencent（腾讯）；Shandong Jining Medical College（山东济宁医学院）
Background: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology based on non-contrast CT (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting. Materials and Methods: The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians's performance. We …
Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training. The trained model generates contextual CT images based on input masks. We quantify the quality of the images by using a fully convolutional network (FCN) score and blurrin…
Florida Atlantic University（佛罗里达大西洋大学）；University of Technology（理工大学）；Babylon Health Directorate（巴比伦卫生局）
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each sl…
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Northwestern University（西北大学）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））
St. Michael’s Hospital（圣迈克尔医院）；University of Toronto（多伦多大学）
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demo…
Vision Transformer for Intracranial Hemorrhage Classification in CT Scans Using an Entropy-Aware Fuzzy Integral Strategy for Adaptive Scan-Level Decision Fusion
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）
deepc GmbH（deepc有限公司）；Technische Universität München（慕尼黑工业大学）
High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few traini…
Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC（先进计算与电子科学组，坎塔布里亚物理研究所（IFCA），CSIC-UC）；AI Technology for Life, Department of Computing and Information Sciences, Utrecht University（人工智能技术与生命，计算与信息科学系，乌得勒支大学）；Department of Radiology, The Netherlands Cancer Institute（放射科，荷兰癌症研究所）；Servicio de Radiología, Hospital Reina Sofía（放射科，雷娜·索菲亚医院）；Servicio de Radiología, Hospital Universitario Rey Juan Carlos（放射科，雷昂·卡洛斯大学医院）；Departamento de Tecnologías de La Información, Universidad CEU San Pablo（信息技术部，CEU桑帕布洛大学）
Intracranial hemorrhages (ICHs) are life-threatening brain injures with a relatively high incidence. In this paper, the automatic algorithm for the detection and classification of ICHs, including localization, is present. The set of binary convolutional neural network-based classifiers with a designed cascade-parallel architecture is used. This automatic system may lead to a distinct decrease in the diagnostic process's duration in acute cases. An average Jaccard coefficient of 53.7 % is achieved on the data from the publicly available head CT dataset CQ500.
We propose Artificial Intelligence Prospective Randomized Observer Blinding Evaluation (AI-PROBE) for quantitative clinical performance evaluation of radiology AI systems within prospective randomized clinical trials. AI-PROBE encompasses a study design and a matching radiology IT infrastructure that randomly blinds radiologists for results provided by AI-based image analysis. To demonstrate the applicability of our evaluation framework, we present a first prospective randomized clinical trial on the effect of Intra-Cranial Hemorrhage (ICH) detection in emergent care head CT on radiology study…
Sberbank AI Lab（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and also conducted clinical experiments involving ten experienced radiologists. Our model achieved strong results on historical data, and significantly outperformed seven radiologist out of ten, while being on par with the remaining three.
SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing
Visual Computing and Artificial Intelligence, Kiel University, Germany（视觉计算与人工智能研究所，基尔大学，德国）；Institute for Medical Informatics and Statistics, Kiel University, Germany（医学信息学与统计研究所，基尔大学，德国）；Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany（神经放射科，马格德堡大学医学学院，德国）；Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Germany（放射学与神经放射学部门，石勒苏益格-荷尔斯泰因大学医院，德国）；Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland（数学与计算机科学学院，亚当·密茨凯维奇大学，波兰）
Research Institute for Signals, Systems and Computational Intelligence, sinc(i), CONICET, FICH-UNL（信号、系统与计算智能研究所 (sinc(i))，国家科学与技术研究理事会 (CONICET)，卢汉国立大学工程学院 (FICH-UNL)）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure. In these cases, CT scans are obtained to diagnose and assess injuries, or guide a certain therapy and intervention. We propose a deep learning based method to reconstruct the skull defect removed during DC performed after TBI from post-operative CT images. This reconstruction is useful in multiple scen…
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）；GlaxoSmithKline（葛兰素史克）
Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no interest in a particular individual's information but rather aim to derive insights at the level of cohorts. Here, we utilize Generative Adversarial Networks (GANs) to create derived medical imaging datasets consisting entirely of synthetic patient data. The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information. We ass…
